Autonomous Navigation with Online Replanning and Recovery Behaviors for Wheeled-Legged Robots using Behavior Trees

Performing autonomous navigation in cluttered and unstructured terrains still remains a challenging task for legged wheeled mobile robots. To accomplish such task, online planners shall incorporate new terrain information perceived while the robot is moving within its environment. While hybrid mobility robots offer high flexibility traversing by leveraging advantages of both locomotion, effective planning actions that transparently combine modes locomotion has not been extensively explored. In this work, we present hierarchical primitive-based planner with wheeled-legged The framework handled Behavior Tree (BT) it takes into account recovery methods to deal possible failures during execution navigation/mobility plan. was evaluated multiple irregular heavily simulated environments randomly generated real-world trials, using CENTAURO platform. With these experiments, demonstrated capabilities without any human intervention, even case collision or failures.